We propose Self-Denoising Monte Carlo Annotation (SCAN), an efficient Process Reward Model (PRM) data synthesis and noise-tolerant learning framework.
Ding
dyyyyyyyy
AI & ML interests
None yet
Recent Activity
updated
a model
about 1 month ago
dyyyyyyyy/Qwen2.5-1.5B-GenRM-WithTemplate
published
a model
about 1 month ago
dyyyyyyyy/Qwen2.5-1.5B-GenRM-WithTemplate
new activity
about 1 month ago
dyyyyyyyy/Qwen2.5-1.5B-GenRM-QueryOnly:Possible issue with the new tokenizer config chat template
Organizations
We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains.
-
Rethinking Negative Instances for Generative Named Entity Recognition
Paper ā¢ 2402.16602 ā¢ Published ā¢ 3 -
dyyyyyyyy/GNER-LLaMA-7B
Text Generation ā¢ 7B ā¢ Updated ā¢ 30 ā¢ 5 -
dyyyyyyyy/GNER-T5-base
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 7 ā¢ 2 -
dyyyyyyyy/GNER-T5-large
Text Generation ā¢ 0.8B ā¢ Updated ā¢ 4 ā¢ 2
We introduce ScaleQuest, a scalable and novel data synthesis method. Project Page: https://scalequest.github.io/
-
Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
Paper ā¢ 2410.18693 ā¢ Published ā¢ 43 -
dyyyyyyyy/ScaleQuest-Math
Viewer ā¢ Updated ā¢ 1M ā¢ 69 ā¢ 23 -
dyyyyyyyy/ScaleQuest-Code
Viewer ā¢ Updated ā¢ 157k ā¢ 13 ā¢ 3 -
dyyyyyyyy/ScaleQuest-Math-Qwen2.5
Viewer ā¢ Updated ā¢ 622k ā¢ 24
We propose Self-Denoising Monte Carlo Annotation (SCAN), an efficient Process Reward Model (PRM) data synthesis and noise-tolerant learning framework.
We introduce ScaleQuest, a scalable and novel data synthesis method. Project Page: https://scalequest.github.io/
-
Unleashing Reasoning Capability of LLMs via Scalable Question Synthesis from Scratch
Paper ā¢ 2410.18693 ā¢ Published ā¢ 43 -
dyyyyyyyy/ScaleQuest-Math
Viewer ā¢ Updated ā¢ 1M ā¢ 69 ā¢ 23 -
dyyyyyyyy/ScaleQuest-Code
Viewer ā¢ Updated ā¢ 157k ā¢ 13 ā¢ 3 -
dyyyyyyyy/ScaleQuest-Math-Qwen2.5
Viewer ā¢ Updated ā¢ 622k ā¢ 24
We introduce GNER, a Generative Named Entity Recognition framework, which demonstrates enhanced zero-shot capabilities across unseen entity domains.
-
Rethinking Negative Instances for Generative Named Entity Recognition
Paper ā¢ 2402.16602 ā¢ Published ā¢ 3 -
dyyyyyyyy/GNER-LLaMA-7B
Text Generation ā¢ 7B ā¢ Updated ā¢ 30 ā¢ 5 -
dyyyyyyyy/GNER-T5-base
Text Generation ā¢ 0.2B ā¢ Updated ā¢ 7 ā¢ 2 -
dyyyyyyyy/GNER-T5-large
Text Generation ā¢ 0.8B ā¢ Updated ā¢ 4 ā¢ 2